Material Type  

 학위논문

 

0016931479

Date and Time of Latest Transaction  

20240214100029

ISBN  

9798379905224

DDC  

004

Author  

Mansoor, Sanaa.

Title/Author  

Generating and Harnessing Learned Embeddings for Protein Design - [electronic resource]

Publish Info  

[S.l.] : University of Washington., 2023

Publish Info  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Material Info  

1 online resource(76 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

General Note  

Advisor: Baker, David.

학위논문주기  

Thesis (Ph.D.)--University of Washington, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Abstracts/Etc  

요약The structure and function of proteins are encoded by their amino acid sequences. The field of protein design aims to uncover the fundamental connection between protein sequence, structure, and function to design novel proteins with important applications in fields such as medicine, biotechnology, and materials science. The complex relationship between protein sequence, structure, and function makes protein design a challenging task. In recent years, learned embeddings have emerged as a powerful tool to help deconvolute this relationship. Learned embeddings can convert high-dimensional protein data, such as protein sequences and structures, into small vectors of biologically relevant information. By capturing all the essential features of a protein in a compact form, embeddings enable the use of machine learning techniques for protein design. My PhD research has focused on generating meaningful learned embeddings of proteins and then harnessing them for various downstream predictions. For studying protein ensembles and protein structure refinement, I developed embeddings through training generative models on two-dimensional structural data, followed by three-dimensional structural modeling. By incorporating sequence information, a joint representation of protein sequence and structure was developed for predicting the effects of single mutations on protein thermal stability. Finally, following the development and success of an accurate structure prediction model, RoseTTAFold, the embeddings learned from this model were used for "zero-shot" or unsupervised prediction of the effect of point mutations on protein stability and function. These successes demonstrate the importance of using learned protein embeddings for protein design and highlight the need for further research in this area to facilitate the creation of novel proteins with desired properties.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Biochemistry.

Index Term-Uncontrolled  

Deep learning

Index Term-Uncontrolled  

Embeddings

Index Term-Uncontrolled  

Protein design

Index Term-Uncontrolled  

Machine learning techniques

Added Entry-Corporate Name  

University of Washington Molecular Engineering and Sciences

Host Item Entry  

Dissertations Abstracts International. 85-01B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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소장사항  

202402 2024

Control Number  

joongbu:643592